Ensemble-based observation impact estimates using the NCEP GFS

Abstract

The impacts of the assimilated observations on the 24-hour forecasts are estimated with the ensemble-based method proposed by Kalnay et al. using an ensemble Kalman filter (EnKF). This method estimates the relative impact of observations in data assimilation similar to the adjoint-based method proposed by Langland and Baker but without using the adjoint model. It is implemented on the National Centers for Environmental Prediction Global Forecasting System EnKF that has been used as part of operational global data assimilation system at NCEP since May 2012. The result quantifies the overall positive impacts of the assimilated observations and the relative importance of the satellite radiance observations compared to other types of observations, especially for the moisture fields. A simple moving localisation based on the average wind, although not optimal, seems to work well. The method is also used to identify the cause of local forecast failure cases in the 24-hour forecasts. Data-denial experiments of the observations identified as producing a negative impact are performed, and forecast errors are reduced as estimated, thus validating the impact estimation.

• data assimilation
• observation impact
• ensemble Kalman filter
• skill dropout
• ensemble sensitivity

Acknowledgements

The authors thank Daryl Kleist (NCEP/EMC) for valuable discussion and his continuous encouragement of this work, and Dr. Mitch Goldberg for his support of this research. Jeff Whitaker (NOAA/ESRL) first developed the EnKF data assimilation system used in this study. Discussions with Ricardo Todling (NASA/GMAO) helped us to understand better this method. The authors are grateful to two anonymous reviewers for their thoughtful comments and suggestions that helped us improve the manuscript. This work was partially supported by a NESDIS/JPSS JPSS Proving Ground (PG) and a Risk Reduction (RR) CICS Grant.